# Data Stats Analysis

Perform statistical tests, hypothesis testing, correlation analysis, and multiple...

- Repository: https://github.com/beita6969/ScienceClaw (1024 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add beita6969/ScienceClaw --skill data-stats-analysis -g -a claude-code -y`
Codex: `npx skills add beita6969/ScienceClaw --skill data-stats-analysis -g -a codex -y`
Cursor: `npx skills add beita6969/ScienceClaw --skill data-stats-analysis -g -a cursor -y`
Gemini CLI: `npx skills add beita6969/ScienceClaw --skill data-stats-analysis -g -a gemini-cli -y`

## Description

Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Statistical Analysis (Universal)

## Overview
This skill enables you to perform rigorous statistical analyses including t-tests, ANOVA, correlation analysis, hypothesis testing, and multiple testing corrections. Unlike cloud-hosted solutions, this skill uses standard Python statistical libraries (**scipy**, **statsmodels**, **numpy**) and executes **locally** in your environment, making it compatible with **ALL LLM providers** including GPT, Gemini, Claude, DeepSeek, and Qwen.

## When to Use This Skill
- Compare means between groups (t-tests, ANOVA)
- Test for correlations between variables
- Perform hypothesis testing with p-value calculation
- Apply multiple testing corrections (FDR, Bonferroni)
- Calculate statistical summaries and confidence intervals
- Test for normality and distribution fitting
- Perform non-parametric tests (Mann-Whitney, Kruskal-Wallis)

## How to Use

### Step 1: Import Required Libraries
```python
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import ttest_ind, mannwhitneyu, pearsonr, spearmanr
from scipy.stats import f_oneway, kruskal, chi2_contingency
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.proportion import proportions_ztest
import warnings
warnings.filterwarnings('ignore')
```

### Step 2: Two-Sample t-Test
```python
# Compare means between two groups
# group1, group2: arrays of numeric values

# Perform independent t-test
t_statistic, p_value = ttest_ind(group1, group2)

print(f"t-statistic: {t_statistic:.4f}")
print(f"p-value: {p_value:.4e}")

if p_value < 0.05:
    print("✅ Significant difference between groups (p < 0.05)")
else:
    print("❌ No significant difference (p >= 0.05)")

The rest: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/data-stats-analysis/SKILL.md